ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations. We propose ProbRes, a post-hoc probabilistic calibration method that explicitly learns and incorporates volatility dynamics into probabilistic forecasting, enabling effective handling of heteroskedastic data. During training, ProbRes employs two architecture-agnostic modules to separately model the conditional mean and conditional volatility. At the inference stage, it generates predictive distributions by resampling normalized residuals. ProbRes is applicable to both univariate and multivariate time series and remains ...
| Authors | Tingting Wang, Yunyi Zhang, Benyou Wang |
|---|---|
| Primary category | stat.ML |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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